{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import cv2\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = pd.read_csv('data/ODIR-5K_Training_Annotations(Updated)_V2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Patient Age</th>\n",
       "      <th>Patient Sex</th>\n",
       "      <th>Left-Fundus</th>\n",
       "      <th>Right-Fundus</th>\n",
       "      <th>Left-Diagnostic Keywords</th>\n",
       "      <th>Right-Diagnostic Keywords</th>\n",
       "      <th>N</th>\n",
       "      <th>D</th>\n",
       "      <th>G</th>\n",
       "      <th>C</th>\n",
       "      <th>A</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "      <th>O</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>Female</td>\n",
       "      <td>0_left.jpg</td>\n",
       "      <td>0_right.jpg</td>\n",
       "      <td>cataract</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>1_left.jpg</td>\n",
       "      <td>1_right.jpg</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>2_left.jpg</td>\n",
       "      <td>2_right.jpg</td>\n",
       "      <td>laser spot，moderate non proliferative retinopathy</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>3_left.jpg</td>\n",
       "      <td>3_right.jpg</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>branch retinal artery occlusion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>Male</td>\n",
       "      <td>4_left.jpg</td>\n",
       "      <td>4_right.jpg</td>\n",
       "      <td>macular epiretinal membrane</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Patient Age Patient Sex Left-Fundus Right-Fundus  \\\n",
       "0   0           69      Female  0_left.jpg  0_right.jpg   \n",
       "1   1           57        Male  1_left.jpg  1_right.jpg   \n",
       "2   2           42        Male  2_left.jpg  2_right.jpg   \n",
       "3   3           66        Male  3_left.jpg  3_right.jpg   \n",
       "4   4           53        Male  4_left.jpg  4_right.jpg   \n",
       "\n",
       "                            Left-Diagnostic Keywords  \\\n",
       "0                                           cataract   \n",
       "1                                      normal fundus   \n",
       "2  laser spot，moderate non proliferative retinopathy   \n",
       "3                                      normal fundus   \n",
       "4                        macular epiretinal membrane   \n",
       "\n",
       "                Right-Diagnostic Keywords  N  D  G  C  A  H  M  O  \n",
       "0                           normal fundus  0  0  0  1  0  0  0  0  \n",
       "1                           normal fundus  1  0  0  0  0  0  0  0  \n",
       "2  moderate non proliferative retinopathy  0  1  0  0  0  0  0  1  \n",
       "3         branch retinal artery occlusion  0  0  0  0  0  0  0  1  \n",
       "4       mild nonproliferative retinopathy  0  1  0  0  0  0  0  1  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = pd.read_csv('data/XYZ_ODIR.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>N</th>\n",
       "      <th>D</th>\n",
       "      <th>G</th>\n",
       "      <th>C</th>\n",
       "      <th>A</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "      <th>O</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>937</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>967</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>988</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>995</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID  N  D  G  C  A  H  M  O\n",
       "0   937  0  0  0  0  0  0  0  0\n",
       "1   967  0  0  0  0  0  0  0  0\n",
       "2   988  0  0  0  0  0  0  0  0\n",
       "3   995  0  0  0  0  0  0  0  0\n",
       "4  1000  0  0  0  0  0  0  0  0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "corr = train.apply(lambda x: x['Left-Diagnostic Keywords'] == x['Right-Diagnostic Keywords'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e8952f198>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(corr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2914\n",
       "2     557\n",
       "3      29\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summ = train.apply(lambda x: x['N'] + x['D'] + x['G'] + x['C'] + x['A'] + x['H'] + x['M'] + x['O'], axis=1)\n",
    "summ.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "ind = []\n",
    "for i in range(len(summ)):\n",
    "    if summ[i] == 3:\n",
    "        ind.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Patient Age</th>\n",
       "      <th>Patient Sex</th>\n",
       "      <th>Left-Fundus</th>\n",
       "      <th>Right-Fundus</th>\n",
       "      <th>Left-Diagnostic Keywords</th>\n",
       "      <th>Right-Diagnostic Keywords</th>\n",
       "      <th>N</th>\n",
       "      <th>D</th>\n",
       "      <th>G</th>\n",
       "      <th>C</th>\n",
       "      <th>A</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "      <th>O</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>188</td>\n",
       "      <td>55</td>\n",
       "      <td>Female</td>\n",
       "      <td>188_left.jpg</td>\n",
       "      <td>188_right.jpg</td>\n",
       "      <td>laser spot，severe proliferative diabetic retin...</td>\n",
       "      <td>cataract</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>305</td>\n",
       "      <td>55</td>\n",
       "      <td>Male</td>\n",
       "      <td>305_left.jpg</td>\n",
       "      <td>305_right.jpg</td>\n",
       "      <td>hypertensive retinopathy,diabetic retinopathy</td>\n",
       "      <td>hypertensive retinopathy，macular epiretinal me...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>624</th>\n",
       "      <td>625</td>\n",
       "      <td>72</td>\n",
       "      <td>Female</td>\n",
       "      <td>625_left.jpg</td>\n",
       "      <td>625_right.jpg</td>\n",
       "      <td>cataract，suspected glaucoma</td>\n",
       "      <td>cataract，branch retinal vein occlusion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>938</td>\n",
       "      <td>76</td>\n",
       "      <td>Male</td>\n",
       "      <td>938_left.jpg</td>\n",
       "      <td>938_right.jpg</td>\n",
       "      <td>dry age-related macular degeneration，myopia re...</td>\n",
       "      <td>diabetic retinopathy，dry age-related macular d...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1007</th>\n",
       "      <td>1020</td>\n",
       "      <td>67</td>\n",
       "      <td>Male</td>\n",
       "      <td>1020_left.jpg</td>\n",
       "      <td>1020_right.jpg</td>\n",
       "      <td>glaucoma，intraretinal hemorrhage</td>\n",
       "      <td>myopia retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1186</th>\n",
       "      <td>1263</td>\n",
       "      <td>62</td>\n",
       "      <td>Male</td>\n",
       "      <td>1263_left.jpg</td>\n",
       "      <td>1263_right.jpg</td>\n",
       "      <td>old central retinal vein occlusion</td>\n",
       "      <td>glaucoma，hypertensive retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1219</th>\n",
       "      <td>1303</td>\n",
       "      <td>49</td>\n",
       "      <td>Male</td>\n",
       "      <td>1303_left.jpg</td>\n",
       "      <td>1303_right.jpg</td>\n",
       "      <td>glaucoma，central retinal vein occlusion</td>\n",
       "      <td>hypertensive retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1265</th>\n",
       "      <td>1410</td>\n",
       "      <td>69</td>\n",
       "      <td>Female</td>\n",
       "      <td>1410_left.jpg</td>\n",
       "      <td>1410_right.jpg</td>\n",
       "      <td>suspected glaucoma，refractive media opacity</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1293</th>\n",
       "      <td>1442</td>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>1442_left.jpg</td>\n",
       "      <td>1442_right.jpg</td>\n",
       "      <td>glaucoma，mild nonproliferative retinopathy，mac...</td>\n",
       "      <td>pigmentation disorder</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1313</th>\n",
       "      <td>1474</td>\n",
       "      <td>79</td>\n",
       "      <td>Female</td>\n",
       "      <td>1474_left.jpg</td>\n",
       "      <td>1474_right.jpg</td>\n",
       "      <td>glaucoma，moderate non proliferative retinopathy</td>\n",
       "      <td>drusen</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>1674</td>\n",
       "      <td>74</td>\n",
       "      <td>Female</td>\n",
       "      <td>1674_left.jpg</td>\n",
       "      <td>1674_right.jpg</td>\n",
       "      <td>moderate non proliferative retinopathy，patholo...</td>\n",
       "      <td>refractive media opacity</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1464</th>\n",
       "      <td>1718</td>\n",
       "      <td>65</td>\n",
       "      <td>Male</td>\n",
       "      <td>1718_left.jpg</td>\n",
       "      <td>1718_right.jpg</td>\n",
       "      <td>moderate non proliferative retinopathy，laser spot</td>\n",
       "      <td>moderate non proliferative retinopathy，patholo...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1465</th>\n",
       "      <td>1731</td>\n",
       "      <td>72</td>\n",
       "      <td>Female</td>\n",
       "      <td>1731_left.jpg</td>\n",
       "      <td>1731_right.jpg</td>\n",
       "      <td>pathological myopia</td>\n",
       "      <td>moderate non proliferative retinopathy，tessell...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1522</th>\n",
       "      <td>1965</td>\n",
       "      <td>62</td>\n",
       "      <td>Female</td>\n",
       "      <td>1965_left.jpg</td>\n",
       "      <td>1965_right.jpg</td>\n",
       "      <td>glaucoma，moderate non proliferative retinopathy</td>\n",
       "      <td>laser spot，moderate non proliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1526</th>\n",
       "      <td>1971</td>\n",
       "      <td>65</td>\n",
       "      <td>Male</td>\n",
       "      <td>1971_left.jpg</td>\n",
       "      <td>1971_right.jpg</td>\n",
       "      <td>suspected glaucoma</td>\n",
       "      <td>moderate non proliferative retinopathy，patholo...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1530</th>\n",
       "      <td>1978</td>\n",
       "      <td>60</td>\n",
       "      <td>Female</td>\n",
       "      <td>1978_left.jpg</td>\n",
       "      <td>1978_right.jpg</td>\n",
       "      <td>lens dust，epiretinal membrane，glaucoma，moderat...</td>\n",
       "      <td>lens dust，glaucoma，epiretinal membrane</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>1985</td>\n",
       "      <td>53</td>\n",
       "      <td>Female</td>\n",
       "      <td>1985_left.jpg</td>\n",
       "      <td>1985_right.jpg</td>\n",
       "      <td>glaucoma，moderate non proliferative retinopathy</td>\n",
       "      <td>myelinated nerve fibers</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537</th>\n",
       "      <td>1994</td>\n",
       "      <td>67</td>\n",
       "      <td>Male</td>\n",
       "      <td>1994_left.jpg</td>\n",
       "      <td>1994_right.jpg</td>\n",
       "      <td>cataract，myelinated nerve fibers，moderate non ...</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1543</th>\n",
       "      <td>2030</td>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>2030_left.jpg</td>\n",
       "      <td>2030_right.jpg</td>\n",
       "      <td>glaucoma</td>\n",
       "      <td>mild nonproliferative retinopathy，glaucoma，vit...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1546</th>\n",
       "      <td>2041</td>\n",
       "      <td>55</td>\n",
       "      <td>Female</td>\n",
       "      <td>2041_left.jpg</td>\n",
       "      <td>2041_right.jpg</td>\n",
       "      <td>glaucoma，wet age-related macular degeneration</td>\n",
       "      <td>tessellated fundus</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1547</th>\n",
       "      <td>2048</td>\n",
       "      <td>63</td>\n",
       "      <td>Male</td>\n",
       "      <td>2048_left.jpg</td>\n",
       "      <td>2048_right.jpg</td>\n",
       "      <td>hypertensive retinopathy</td>\n",
       "      <td>glaucoma，old central retinal vein occlusion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1550</th>\n",
       "      <td>2063</td>\n",
       "      <td>77</td>\n",
       "      <td>Male</td>\n",
       "      <td>2063_left.jpg</td>\n",
       "      <td>2063_right.jpg</td>\n",
       "      <td>glaucoma，lens dust</td>\n",
       "      <td>glaucoma，moderate non proliferative retinopath...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>2078</td>\n",
       "      <td>42</td>\n",
       "      <td>Female</td>\n",
       "      <td>2078_left.jpg</td>\n",
       "      <td>2078_right.jpg</td>\n",
       "      <td>diabetic retinopathy，wet age-related macular d...</td>\n",
       "      <td>diabetic retinopathy，macular epiretinal membrane</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1562</th>\n",
       "      <td>2100</td>\n",
       "      <td>31</td>\n",
       "      <td>Male</td>\n",
       "      <td>2100_left.jpg</td>\n",
       "      <td>2100_right.jpg</td>\n",
       "      <td>moderate non proliferative retinopathy，refract...</td>\n",
       "      <td>cataract，moderate non proliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1569</th>\n",
       "      <td>2107</td>\n",
       "      <td>78</td>\n",
       "      <td>Male</td>\n",
       "      <td>2107_left.jpg</td>\n",
       "      <td>2107_right.jpg</td>\n",
       "      <td>cataract</td>\n",
       "      <td>laser spot，moderate non proliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1628</th>\n",
       "      <td>2168</td>\n",
       "      <td>64</td>\n",
       "      <td>Female</td>\n",
       "      <td>2168_left.jpg</td>\n",
       "      <td>2168_right.jpg</td>\n",
       "      <td>cataract</td>\n",
       "      <td>cataract，laser spot，moderate non proliferative...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1658</th>\n",
       "      <td>2200</td>\n",
       "      <td>64</td>\n",
       "      <td>Female</td>\n",
       "      <td>2200_left.jpg</td>\n",
       "      <td>2200_right.jpg</td>\n",
       "      <td>cataract</td>\n",
       "      <td>moderate non proliferative retinopathy，laser spot</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>2282</td>\n",
       "      <td>64</td>\n",
       "      <td>Male</td>\n",
       "      <td>2282_left.jpg</td>\n",
       "      <td>2282_right.jpg</td>\n",
       "      <td>severe proliferative diabetic retinopathy</td>\n",
       "      <td>cataract，vitreous degeneration</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3281</th>\n",
       "      <td>4435</td>\n",
       "      <td>67</td>\n",
       "      <td>Male</td>\n",
       "      <td>4435_left.jpg</td>\n",
       "      <td>4435_right.jpg</td>\n",
       "      <td>post retinal laser surgery，moderate non prolif...</td>\n",
       "      <td>post retinal laser surgery，proliferative diabe...</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        ID  Patient Age Patient Sex    Left-Fundus    Right-Fundus  \\\n",
       "188    188           55      Female   188_left.jpg   188_right.jpg   \n",
       "304    305           55        Male   305_left.jpg   305_right.jpg   \n",
       "624    625           72      Female   625_left.jpg   625_right.jpg   \n",
       "935    938           76        Male   938_left.jpg   938_right.jpg   \n",
       "1007  1020           67        Male  1020_left.jpg  1020_right.jpg   \n",
       "1186  1263           62        Male  1263_left.jpg  1263_right.jpg   \n",
       "1219  1303           49        Male  1303_left.jpg  1303_right.jpg   \n",
       "1265  1410           69      Female  1410_left.jpg  1410_right.jpg   \n",
       "1293  1442           63        Male  1442_left.jpg  1442_right.jpg   \n",
       "1313  1474           79      Female  1474_left.jpg  1474_right.jpg   \n",
       "1455  1674           74      Female  1674_left.jpg  1674_right.jpg   \n",
       "1464  1718           65        Male  1718_left.jpg  1718_right.jpg   \n",
       "1465  1731           72      Female  1731_left.jpg  1731_right.jpg   \n",
       "1522  1965           62      Female  1965_left.jpg  1965_right.jpg   \n",
       "1526  1971           65        Male  1971_left.jpg  1971_right.jpg   \n",
       "1530  1978           60      Female  1978_left.jpg  1978_right.jpg   \n",
       "1532  1985           53      Female  1985_left.jpg  1985_right.jpg   \n",
       "1537  1994           67        Male  1994_left.jpg  1994_right.jpg   \n",
       "1543  2030           66        Male  2030_left.jpg  2030_right.jpg   \n",
       "1546  2041           55      Female  2041_left.jpg  2041_right.jpg   \n",
       "1547  2048           63        Male  2048_left.jpg  2048_right.jpg   \n",
       "1550  2063           77        Male  2063_left.jpg  2063_right.jpg   \n",
       "1556  2078           42      Female  2078_left.jpg  2078_right.jpg   \n",
       "1562  2100           31        Male  2100_left.jpg  2100_right.jpg   \n",
       "1569  2107           78        Male  2107_left.jpg  2107_right.jpg   \n",
       "1628  2168           64      Female  2168_left.jpg  2168_right.jpg   \n",
       "1658  2200           64      Female  2200_left.jpg  2200_right.jpg   \n",
       "1705  2282           64        Male  2282_left.jpg  2282_right.jpg   \n",
       "3281  4435           67        Male  4435_left.jpg  4435_right.jpg   \n",
       "\n",
       "                               Left-Diagnostic Keywords  \\\n",
       "188   laser spot，severe proliferative diabetic retin...   \n",
       "304       hypertensive retinopathy,diabetic retinopathy   \n",
       "624                         cataract，suspected glaucoma   \n",
       "935   dry age-related macular degeneration，myopia re...   \n",
       "1007                   glaucoma，intraretinal hemorrhage   \n",
       "1186                 old central retinal vein occlusion   \n",
       "1219            glaucoma，central retinal vein occlusion   \n",
       "1265        suspected glaucoma，refractive media opacity   \n",
       "1293  glaucoma，mild nonproliferative retinopathy，mac...   \n",
       "1313    glaucoma，moderate non proliferative retinopathy   \n",
       "1455  moderate non proliferative retinopathy，patholo...   \n",
       "1464  moderate non proliferative retinopathy，laser spot   \n",
       "1465                                pathological myopia   \n",
       "1522    glaucoma，moderate non proliferative retinopathy   \n",
       "1526                                 suspected glaucoma   \n",
       "1530  lens dust，epiretinal membrane，glaucoma，moderat...   \n",
       "1532    glaucoma，moderate non proliferative retinopathy   \n",
       "1537  cataract，myelinated nerve fibers，moderate non ...   \n",
       "1543                                           glaucoma   \n",
       "1546      glaucoma，wet age-related macular degeneration   \n",
       "1547                           hypertensive retinopathy   \n",
       "1550                                 glaucoma，lens dust   \n",
       "1556  diabetic retinopathy，wet age-related macular d...   \n",
       "1562  moderate non proliferative retinopathy，refract...   \n",
       "1569                                           cataract   \n",
       "1628                                           cataract   \n",
       "1658                                           cataract   \n",
       "1705          severe proliferative diabetic retinopathy   \n",
       "3281  post retinal laser surgery，moderate non prolif...   \n",
       "\n",
       "                              Right-Diagnostic Keywords  N  D  G  C  A  H  M  \\\n",
       "188                                            cataract  0  1  0  1  0  0  0   \n",
       "304   hypertensive retinopathy，macular epiretinal me...  0  1  0  0  0  1  0   \n",
       "624              cataract，branch retinal vein occlusion  0  0  1  1  0  0  0   \n",
       "935   diabetic retinopathy，dry age-related macular d...  0  1  0  0  1  0  1   \n",
       "1007                                 myopia retinopathy  0  0  1  0  0  0  1   \n",
       "1186                  glaucoma，hypertensive retinopathy  0  0  1  0  0  1  0   \n",
       "1219                           hypertensive retinopathy  0  0  1  0  0  1  0   \n",
       "1265                  mild nonproliferative retinopathy  0  1  1  0  0  0  0   \n",
       "1293                              pigmentation disorder  0  1  1  0  0  0  0   \n",
       "1313                                             drusen  0  1  1  0  0  0  0   \n",
       "1455                           refractive media opacity  0  1  0  0  0  0  1   \n",
       "1464  moderate non proliferative retinopathy，patholo...  0  1  0  0  0  0  1   \n",
       "1465  moderate non proliferative retinopathy，tessell...  0  1  0  0  0  0  1   \n",
       "1522  laser spot，moderate non proliferative retinopathy  0  1  1  0  0  0  0   \n",
       "1526  moderate non proliferative retinopathy，patholo...  0  1  1  0  0  0  1   \n",
       "1530             lens dust，glaucoma，epiretinal membrane  0  1  1  0  0  0  0   \n",
       "1532                            myelinated nerve fibers  0  1  1  0  0  0  0   \n",
       "1537                  mild nonproliferative retinopathy  0  1  0  1  0  0  0   \n",
       "1543  mild nonproliferative retinopathy，glaucoma，vit...  0  1  1  0  0  0  0   \n",
       "1546                                 tessellated fundus  0  0  1  0  1  0  0   \n",
       "1547        glaucoma，old central retinal vein occlusion  0  0  1  0  0  1  0   \n",
       "1550  glaucoma，moderate non proliferative retinopath...  0  1  1  0  0  0  0   \n",
       "1556   diabetic retinopathy，macular epiretinal membrane  0  1  0  0  1  0  0   \n",
       "1562    cataract，moderate non proliferative retinopathy  0  1  0  1  0  0  0   \n",
       "1569  laser spot，moderate non proliferative retinopathy  0  1  0  1  0  0  0   \n",
       "1628  cataract，laser spot，moderate non proliferative...  0  1  0  1  0  0  0   \n",
       "1658  moderate non proliferative retinopathy，laser spot  0  1  0  1  0  0  0   \n",
       "1705                     cataract，vitreous degeneration  0  1  0  1  0  0  0   \n",
       "3281  post retinal laser surgery，proliferative diabe...  0  1  0  1  0  0  0   \n",
       "\n",
       "      O  \n",
       "188   1  \n",
       "304   1  \n",
       "624   1  \n",
       "935   0  \n",
       "1007  1  \n",
       "1186  1  \n",
       "1219  1  \n",
       "1265  1  \n",
       "1293  1  \n",
       "1313  1  \n",
       "1455  1  \n",
       "1464  1  \n",
       "1465  1  \n",
       "1522  1  \n",
       "1526  0  \n",
       "1530  1  \n",
       "1532  1  \n",
       "1537  1  \n",
       "1543  1  \n",
       "1546  1  \n",
       "1547  1  \n",
       "1550  1  \n",
       "1556  1  \n",
       "1562  1  \n",
       "1569  1  \n",
       "1628  1  \n",
       "1658  1  \n",
       "1705  1  \n",
       "3281  1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.iloc[ind]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Чекнем контрольные суммы лейблов"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1140\n",
      "1128\n",
      "215\n",
      "212\n",
      "164\n",
      "103\n",
      "174\n",
      "979\n"
     ]
    }
   ],
   "source": [
    "labels = ['N', 'D', 'G', 'C', 'A', 'H', 'M', 'O']\n",
    "for label in labels:\n",
    "    print(train[label].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "left_unique_full = train['Left-Diagnostic Keywords'].unique()\n",
    "right_unique_full = train['Right-Diagnostic Keywords'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "left_unique = []\n",
    "right_unique = []\n",
    "for label in left_unique_full:\n",
    "    left_unique.extend(label.split('，'))\n",
    "for label in right_unique_full:\n",
    "    right_unique.extend(label.split('，'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "102"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left_unique = list(set(left_unique))\n",
    "right_unique = list(set(right_unique))\n",
    "unique = []\n",
    "for label in left_unique:\n",
    "    unique.extend(label.split(','))\n",
    "for label in right_unique:\n",
    "    unique.extend(label.split(','))\n",
    "unique_diagnosis = list(set(unique))\n",
    "len(unique_diagnosis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no fundus image\n",
      "normal fundus\n",
      "tessellated fundus\n",
      "fundus laser photocoagulation spots\n"
     ]
    }
   ],
   "source": [
    "for diagnosis in unique_diagnosis:\n",
    "    if 'fundus' in diagnosis:\n",
    "        print(diagnosis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dry age-related macular degeneration\n",
      "wet age-related macular degeneration\n",
      "age-related macular degeneration\n"
     ]
    }
   ],
   "source": [
    "for diagnosis in unique_diagnosis:\n",
    "    if 'macular degeneration' in diagnosis:\n",
    "        print(diagnosis)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Хуевый путь матчинга картинок и лейблов был представлен выше, начнем нормально - от лейблов к кейвордам"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train['all_keywords'] = train.apply(lambda x: x['Left-Diagnostic Keywords'] + '，' + x['Right-Diagnostic Keywords'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Patient Age</th>\n",
       "      <th>Patient Sex</th>\n",
       "      <th>Left-Fundus</th>\n",
       "      <th>Right-Fundus</th>\n",
       "      <th>Left-Diagnostic Keywords</th>\n",
       "      <th>Right-Diagnostic Keywords</th>\n",
       "      <th>N</th>\n",
       "      <th>D</th>\n",
       "      <th>G</th>\n",
       "      <th>C</th>\n",
       "      <th>A</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "      <th>O</th>\n",
       "      <th>all_keywords</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>69</td>\n",
       "      <td>Female</td>\n",
       "      <td>0_left.jpg</td>\n",
       "      <td>0_right.jpg</td>\n",
       "      <td>cataract</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>cataract，normal fundus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>57</td>\n",
       "      <td>Male</td>\n",
       "      <td>1_left.jpg</td>\n",
       "      <td>1_right.jpg</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>normal fundus，normal fundus</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>42</td>\n",
       "      <td>Male</td>\n",
       "      <td>2_left.jpg</td>\n",
       "      <td>2_right.jpg</td>\n",
       "      <td>laser spot，moderate non proliferative retinopathy</td>\n",
       "      <td>moderate non proliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>laser spot，moderate non proliferative retinopa...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>Male</td>\n",
       "      <td>3_left.jpg</td>\n",
       "      <td>3_right.jpg</td>\n",
       "      <td>normal fundus</td>\n",
       "      <td>branch retinal artery occlusion</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>normal fundus，branch retinal artery occlusion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>Male</td>\n",
       "      <td>4_left.jpg</td>\n",
       "      <td>4_right.jpg</td>\n",
       "      <td>macular epiretinal membrane</td>\n",
       "      <td>mild nonproliferative retinopathy</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>macular epiretinal membrane，mild nonproliferat...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Patient Age Patient Sex Left-Fundus Right-Fundus  \\\n",
       "0   0           69      Female  0_left.jpg  0_right.jpg   \n",
       "1   1           57        Male  1_left.jpg  1_right.jpg   \n",
       "2   2           42        Male  2_left.jpg  2_right.jpg   \n",
       "3   3           66        Male  3_left.jpg  3_right.jpg   \n",
       "4   4           53        Male  4_left.jpg  4_right.jpg   \n",
       "\n",
       "                            Left-Diagnostic Keywords  \\\n",
       "0                                           cataract   \n",
       "1                                      normal fundus   \n",
       "2  laser spot，moderate non proliferative retinopathy   \n",
       "3                                      normal fundus   \n",
       "4                        macular epiretinal membrane   \n",
       "\n",
       "                Right-Diagnostic Keywords  N  D  G  C  A  H  M  O  \\\n",
       "0                           normal fundus  0  0  0  1  0  0  0  0   \n",
       "1                           normal fundus  1  0  0  0  0  0  0  0   \n",
       "2  moderate non proliferative retinopathy  0  1  0  0  0  0  0  1   \n",
       "3         branch retinal artery occlusion  0  0  0  0  0  0  0  1   \n",
       "4       mild nonproliferative retinopathy  0  1  0  0  0  0  0  1   \n",
       "\n",
       "                                        all_keywords  \n",
       "0                             cataract，normal fundus  \n",
       "1                        normal fundus，normal fundus  \n",
       "2  laser spot，moderate non proliferative retinopa...  \n",
       "3      normal fundus，branch retinal artery occlusion  \n",
       "4  macular epiretinal membrane，mild nonproliferat...  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "normal = []\n",
    "diabetic = []\n",
    "glaucoma = []\n",
    "cataract = []\n",
    "amd = []\n",
    "hypertension = []\n",
    "myopia = []\n",
    "other = []\n",
    "for index, row in train.iterrows():\n",
    "    if row['N'] == 1:\n",
    "        normal.extend(row['all_keywords'].split('，'))\n",
    "    if row['D'] == 1:\n",
    "        diabetic.extend(row['all_keywords'].split('，'))\n",
    "    if row['G'] == 1:\n",
    "        glaucoma.extend(row['all_keywords'].split('，'))\n",
    "    if row['C'] == 1:\n",
    "        cataract.extend(row['all_keywords'].split('，'))\n",
    "    if row['A'] == 1:\n",
    "        amd.extend(row['all_keywords'].split('，'))\n",
    "    if row['H'] == 1:\n",
    "        hypertension.extend(row['all_keywords'].split('，'))\n",
    "    if row['M'] == 1:\n",
    "        myopia.extend(row['all_keywords'].split('，'))\n",
    "    if row['O'] == 1:\n",
    "        other.extend(row['all_keywords'].split('，'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Начнем по порядку с примеров где оба глаза здоровых"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['low image quality', 'lens dust', 'normal fundus']\n"
     ]
    }
   ],
   "source": [
    "normal = list(set(normal))\n",
    "print(normal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Че? Ща посмотрим"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "dust = train.apply(lambda x:  'lens dust' in x['all_keywords'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    3235\n",
       "True      265\n",
       "dtype: int64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dust.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "265 примеров с пылью, круто. Посмотрим, на сколько критично"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID                                                                          20\n",
      "Patient Age                                                                 64\n",
      "Patient Sex                                                             Female\n",
      "Left-Fundus                                                        20_left.jpg\n",
      "Right-Fundus                                                      20_right.jpg\n",
      "Left-Diagnostic Keywords                     rhegmatogenous retinal detachment\n",
      "Right-Diagnostic Keywords                              lens dust，normal fundus\n",
      "N                                                                            0\n",
      "D                                                                            0\n",
      "G                                                                            0\n",
      "C                                                                            0\n",
      "A                                                                            0\n",
      "H                                                                            0\n",
      "M                                                                            0\n",
      "O                                                                            1\n",
      "all_keywords                 rhegmatogenous retinal detachment，lens dust，no...\n",
      "Name: 20, dtype: object\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(dust)):\n",
    "    if dust[i]:\n",
    "        print(train.iloc[i])\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9e86f0a080>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = cv2.imread('data/ODIR-5K_Training_Images/20_right.jpg')[..., :: -1]\n",
    "plt.imshow(im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Хуй знает, вроде норм, пока оставим, тем более их довольно много. Глянем че там по качеству"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "low = train.apply(lambda x:  'low image quality' in x['all_keywords'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    3481\n",
       "True       19\n",
       "dtype: int64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "low.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ну этих хотя бы поменьше"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID                                                                       372\n",
      "Patient Age                                                               52\n",
      "Patient Sex                                                           Female\n",
      "Left-Fundus                                                     372_left.jpg\n",
      "Right-Fundus                                                   372_right.jpg\n",
      "Left-Diagnostic Keywords                       low image quality,maculopathy\n",
      "Right-Diagnostic Keywords                                  low image quality\n",
      "N                                                                          0\n",
      "D                                                                          0\n",
      "G                                                                          0\n",
      "C                                                                          0\n",
      "A                                                                          0\n",
      "H                                                                          0\n",
      "M                                                                          0\n",
      "O                                                                          1\n",
      "all_keywords                 low image quality,maculopathy，low image quality\n",
      "Name: 371, dtype: object\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(low)):\n",
    "    if low[i]:\n",
    "        print(train.iloc[i])\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f9e86d52320>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "im = cv2.imread('data/ODIR-5K_Training_Images/372_left.jpg')[..., :: -1]\n",
    "plt.imshow(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['normal fundus']\n"
     ]
    }
   ],
   "source": [
    "del normal[:1]\n",
    "print(normal)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "В общем какие-то испорченные примеры, выкинем их нахуй видимо. Переходим ко второму пункту - диабетическая ретинопатия"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "severe nonproliferative retinopathy\n",
      "suspected moderate non proliferative retinopathy\n",
      "suspicious diabetic retinopathy\n",
      "severe proliferative diabetic retinopathy\n",
      "moderate non proliferative retinopathy\n",
      "mild nonproliferative retinopathy\n",
      "proliferative diabetic retinopathy\n",
      "suspected diabetic retinopathy\n",
      "diabetic retinopathy\n",
      "hypertensive retinopathy\n",
      "myopia retinopathy\n"
     ]
    }
   ],
   "source": [
    "dummy = []\n",
    "for label in diabetic:\n",
    "    dummy.extend(label.split(','))\n",
    "diabetic = list(set(dummy))\n",
    "for label in diabetic:\n",
    "    if 'retinopathy' in label:\n",
    "        print(label)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тут все вроде как относится к диабетической ретинопатии, кроме двух. Чекнем"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID                                                                         375\n",
      "Patient Age                                                                 64\n",
      "Patient Sex                                                               Male\n",
      "Left-Fundus                                                       375_left.jpg\n",
      "Right-Fundus                                                     375_right.jpg\n",
      "Left-Diagnostic Keywords       punctate inner choroidopathy，myopia retinopathy\n",
      "Right-Diagnostic Keywords      punctate inner choroidopathy，myopia retinopathy\n",
      "N                                                                            0\n",
      "D                                                                            0\n",
      "G                                                                            0\n",
      "C                                                                            0\n",
      "A                                                                            0\n",
      "H                                                                            0\n",
      "M                                                                            1\n",
      "O                                                                            1\n",
      "all_keywords                 punctate inner choroidopathy，myopia retinopath...\n",
      "Name: 374, dtype: object\n"
     ]
    }
   ],
   "source": [
    "mr = train.apply(lambda x:  'myopia retinopathy' in x['all_keywords'], axis=1)\n",
    "for i in range(len(mr)):\n",
    "    if mr[i]:\n",
    "        print(train.iloc[i])\n",
    "        break"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Да, это относится к миопии"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ID                                                                          11\n",
      "Patient Age                                                                 60\n",
      "Patient Sex                                                             Female\n",
      "Left-Fundus                                                        11_left.jpg\n",
      "Right-Fundus                                                      11_right.jpg\n",
      "Left-Diagnostic Keywords     moderate non proliferative retinopathy，hyperte...\n",
      "Right-Diagnostic Keywords    moderate non proliferative retinopathy，hyperte...\n",
      "N                                                                            0\n",
      "D                                                                            1\n",
      "G                                                                            0\n",
      "C                                                                            0\n",
      "A                                                                            0\n",
      "H                                                                            1\n",
      "M                                                                            0\n",
      "O                                                                            0\n",
      "all_keywords                 moderate non proliferative retinopathy，hyperte...\n",
      "Name: 11, dtype: object\n"
     ]
    }
   ],
   "source": [
    "mr = train.apply(lambda x:  'hypertensive retinopathy' in x['all_keywords'], axis=1)\n",
    "for i in range(len(mr)):\n",
    "    if mr[i]:\n",
    "        print(train.iloc[i])\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['suspected moderate non proliferative retinopathy', 'suspicious diabetic retinopathy', 'severe proliferative diabetic retinopathy', 'moderate non proliferative retinopathy', 'mild nonproliferative retinopathy', 'proliferative diabetic retinopathy', 'suspected diabetic retinopathy', 'diabetic retinopathy', 'myopia retinopathy']\n"
     ]
    }
   ],
   "source": [
    "dummy = []\n",
    "for label in diabetic:\n",
    "    if 'retinopathy' in label:\n",
    "        dummy.append(label)\n",
    "del dummy[0]\n",
    "del dummy[-2]\n",
    "diabetic = dummy\n",
    "print(diabetic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "А эта хуйня к гипертензии. Значит все норм, идем дальше"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy = []\n",
    "for label in glaucoma:\n",
    "    dummy.extend(label.split(','))\n",
    "glaucoma = list(set(dummy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['suspected glaucoma', 'glaucoma']\n"
     ]
    }
   ],
   "source": [
    "dummy = []\n",
    "for label in glaucoma:\n",
    "    if 'glaucoma' in label:\n",
    "        dummy.append(label)\n",
    "glaucoma = dummy\n",
    "print(glaucoma)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Тут вроде тоже все понятно, идем дальше"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy = []\n",
    "for label in cataract:\n",
    "    dummy.extend(label.split(','))\n",
    "cataract = list(set(dummy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cataract', 'suspected cataract']\n"
     ]
    }
   ],
   "source": [
    "dummy = []\n",
    "for label in cataract:\n",
    "    if 'cataract' in label:\n",
    "        dummy.append(label)\n",
    "cataract = dummy\n",
    "print(cataract)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заебок"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy = []\n",
    "for label in amd:\n",
    "    dummy.extend(label.split(','))\n",
    "amd = list(set(dummy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dry age-related macular degeneration', 'wet age-related macular degeneration', 'age-related macular degeneration']\n"
     ]
    }
   ],
   "source": [
    "dummy = []\n",
    "for label in amd:\n",
    "    if 'degeneration' in label:\n",
    "        dummy.append(label)\n",
    "amd = dummy\n",
    "print(amd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Четко"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy = []\n",
    "for label in hypertension:\n",
    "    dummy.extend(label.split(','))\n",
    "hypertension = list(set(dummy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['hypertensive retinopathy']\n"
     ]
    }
   ],
   "source": [
    "dummy = []\n",
    "for label in hypertension:\n",
    "    if 'hyper' in label:\n",
    "        dummy.append(label)\n",
    "hypertension = dummy\n",
    "print(hypertension)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Еще одна ретинопатия. Ладно, дальше"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy = []\n",
    "for label in myopia:\n",
    "    dummy.extend(label.split(','))\n",
    "myopia = list(set(dummy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['myopic retinopathy', 'myopic maculopathy', 'pathological myopia', 'myopia retinopathy']\n"
     ]
    }
   ],
   "source": [
    "dummy = []\n",
    "for label in myopia:\n",
    "    if 'my' in label:\n",
    "        dummy.append(label)\n",
    "myopia = dummy\n",
    "print(myopia)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Составим итоговую таблицу. Сначала дропнем хуевые"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(len(low)):\n",
    "    if low[i]:\n",
    "        train = train.drop(index=i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 3481 entries, 0 to 3499\n",
      "Data columns (total 16 columns):\n",
      "ID                           3481 non-null int64\n",
      "Patient Age                  3481 non-null int64\n",
      "Patient Sex                  3481 non-null object\n",
      "Left-Fundus                  3481 non-null object\n",
      "Right-Fundus                 3481 non-null object\n",
      "Left-Diagnostic Keywords     3481 non-null object\n",
      "Right-Diagnostic Keywords    3481 non-null object\n",
      "N                            3481 non-null int64\n",
      "D                            3481 non-null int64\n",
      "G                            3481 non-null int64\n",
      "C                            3481 non-null int64\n",
      "A                            3481 non-null int64\n",
      "H                            3481 non-null int64\n",
      "M                            3481 non-null int64\n",
      "O                            3481 non-null int64\n",
      "all_keywords                 3481 non-null object\n",
      "dtypes: int64(10), object(6)\n",
      "memory usage: 462.3+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "eye_by_eye = pd.DataFrame(columns=['id', 'N', 'D', 'G', 'C', 'A', 'H', 'M', 'O'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "3481it [00:15, 219.97it/s]\n"
     ]
    }
   ],
   "source": [
    "for index, row in tqdm(train.iterrows()):\n",
    "    new_row = {'id': row['Left-Fundus'], 'N':0, 'D':0, 'G':0, 'C':0, 'A':0, 'H':0, 'M':0, 'O':0}\n",
    "    kwords = row['Left-Diagnostic Keywords'].split('，')\n",
    "    dummy = []\n",
    "    for word in kwords:\n",
    "        dummy.extend(word.split(','))\n",
    "    kwords = dummy\n",
    "    summ = 0\n",
    "    for word in kwords:\n",
    "        if word in normal:\n",
    "            new_row['N'] = 1\n",
    "            summ += 1\n",
    "        elif word in diabetic:\n",
    "            new_row['D'] = 1\n",
    "            summ += 1\n",
    "        elif word in glaucoma:\n",
    "            new_row['G'] = 1\n",
    "            summ += 1\n",
    "        elif word in cataract:\n",
    "            new_row['C'] = 1\n",
    "            summ += 1\n",
    "        elif word in amd:\n",
    "            new_row['A'] = 1\n",
    "            summ += 1\n",
    "        elif word in hypertension:\n",
    "            new_row['H'] = 1\n",
    "            summ += 1\n",
    "        elif word in myopia:\n",
    "            new_row['M'] = 1\n",
    "            summ += 1\n",
    "        else:\n",
    "            if word == 'lens dust':\n",
    "                continue\n",
    "            new_row['O'] = 1\n",
    "            summ += 1\n",
    "    if summ == 0:\n",
    "        continue\n",
    "    eye_by_eye = eye_by_eye.append(new_row, ignore_index=True)\n",
    "\n",
    "    new_row = {'id': row['Right-Fundus'], 'N':0, 'D':0, 'G':0, 'C':0, 'A':0, 'H':0, 'M':0, 'O':0}\n",
    "    kwords = row['Right-Diagnostic Keywords'].split('，')\n",
    "    dummy = []\n",
    "    for word in kwords:\n",
    "        dummy.extend(word.split(','))\n",
    "    kwords = dummy\n",
    "    summ = 0\n",
    "    for word in kwords:\n",
    "        if word in normal:\n",
    "            new_row['N'] = 1\n",
    "            summ += 1\n",
    "        elif word in diabetic:\n",
    "            new_row['D'] = 1\n",
    "            summ += 1\n",
    "        elif word in glaucoma:\n",
    "            new_row['G'] = 1\n",
    "            summ += 1\n",
    "        elif word in cataract:\n",
    "            new_row['C'] = 1\n",
    "            summ += 1\n",
    "        elif word in amd:\n",
    "            new_row['A'] = 1\n",
    "            summ += 1\n",
    "        elif word in hypertension:\n",
    "            new_row['H'] = 1\n",
    "            summ += 1\n",
    "        elif word in myopia:\n",
    "            new_row['M'] = 1\n",
    "            summ += 1\n",
    "        else:\n",
    "            if word == 'lens dust':\n",
    "                continue\n",
    "            new_row['O'] = 1\n",
    "            summ += 1\n",
    "    if summ == 0:\n",
    "        continue\n",
    "    eye_by_eye = eye_by_eye.append(new_row, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>N</th>\n",
       "      <th>D</th>\n",
       "      <th>G</th>\n",
       "      <th>C</th>\n",
       "      <th>A</th>\n",
       "      <th>H</th>\n",
       "      <th>M</th>\n",
       "      <th>O</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0_right.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1_left.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1_right.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3_left.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>3_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>6_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>6_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>7_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>7_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>8_left.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8_right.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>9_left.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>9_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10_right.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>11_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>11_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>12_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>12_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>13_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>13_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>14_left.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>14_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>280</th>\n",
       "      <td>140_left.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>281</th>\n",
       "      <td>140_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>141_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>283</th>\n",
       "      <td>141_right.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>142_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>285</th>\n",
       "      <td>142_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>286</th>\n",
       "      <td>143_left.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>287</th>\n",
       "      <td>143_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>144_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>144_right.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>145_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291</th>\n",
       "      <td>145_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>292</th>\n",
       "      <td>146_left.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>293</th>\n",
       "      <td>146_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>294</th>\n",
       "      <td>147_left.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>295</th>\n",
       "      <td>147_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>148_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>148_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>149_left.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>149_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>150_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>150_right.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>151_left.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>151_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>304</th>\n",
       "      <td>152_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>305</th>\n",
       "      <td>152_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306</th>\n",
       "      <td>153_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>307</th>\n",
       "      <td>153_right.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308</th>\n",
       "      <td>154_left.jpg</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>309</th>\n",
       "      <td>154_right.jpg</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>310 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                id  N  D  G  C  A  H  M  O\n",
       "0       0_left.jpg  0  0  0  1  0  0  0  0\n",
       "1      0_right.jpg  1  0  0  0  0  0  0  0\n",
       "2       1_left.jpg  1  0  0  0  0  0  0  0\n",
       "3      1_right.jpg  1  0  0  0  0  0  0  0\n",
       "4       2_left.jpg  0  1  0  0  0  0  0  1\n",
       "5      2_right.jpg  0  1  0  0  0  0  0  0\n",
       "6       3_left.jpg  1  0  0  0  0  0  0  0\n",
       "7      3_right.jpg  0  0  0  0  0  0  0  1\n",
       "8       4_left.jpg  0  0  0  0  0  0  0  1\n",
       "9      4_right.jpg  0  1  0  0  0  0  0  0\n",
       "10      5_left.jpg  0  1  0  0  0  0  0  0\n",
       "11     5_right.jpg  0  1  0  0  0  0  0  0\n",
       "12      6_left.jpg  0  0  0  0  0  0  0  1\n",
       "13     6_right.jpg  0  1  0  0  0  0  0  1\n",
       "14      7_left.jpg  0  0  0  0  0  0  0  1\n",
       "15     7_right.jpg  0  1  0  0  0  0  0  0\n",
       "16      8_left.jpg  1  0  0  0  0  0  0  0\n",
       "17     8_right.jpg  1  0  0  0  0  0  0  0\n",
       "18      9_left.jpg  1  0  0  0  0  0  0  0\n",
       "19     9_right.jpg  0  0  0  0  0  0  0  1\n",
       "20     10_left.jpg  0  0  0  0  0  0  0  1\n",
       "21    10_right.jpg  1  0  0  0  0  0  0  0\n",
       "22     11_left.jpg  0  1  0  0  0  1  0  0\n",
       "23    11_right.jpg  0  1  0  0  0  1  0  0\n",
       "24     12_left.jpg  0  0  0  0  0  0  0  1\n",
       "25    12_right.jpg  0  0  0  0  0  0  0  1\n",
       "26     13_left.jpg  0  0  0  0  0  0  1  0\n",
       "27    13_right.jpg  0  0  0  0  0  0  1  0\n",
       "28     14_left.jpg  1  0  0  0  0  0  0  0\n",
       "29    14_right.jpg  0  0  0  0  0  0  0  1\n",
       "..             ... .. .. .. .. .. .. .. ..\n",
       "280   140_left.jpg  1  0  0  0  0  0  0  0\n",
       "281  140_right.jpg  0  0  0  0  0  0  0  1\n",
       "282   141_left.jpg  0  0  0  0  0  0  0  1\n",
       "283  141_right.jpg  1  0  0  0  0  0  0  0\n",
       "284   142_left.jpg  0  0  0  0  0  0  0  1\n",
       "285  142_right.jpg  0  0  0  0  0  0  0  1\n",
       "286   143_left.jpg  1  0  0  0  0  0  0  0\n",
       "287  143_right.jpg  0  0  0  0  0  0  0  1\n",
       "288   144_left.jpg  0  0  0  0  0  0  1  0\n",
       "289  144_right.jpg  1  0  0  0  0  0  0  0\n",
       "290   145_left.jpg  0  0  0  0  0  0  1  0\n",
       "291  145_right.jpg  0  0  0  0  0  0  1  0\n",
       "292   146_left.jpg  1  0  0  0  0  0  0  0\n",
       "293  146_right.jpg  0  0  0  0  0  0  0  1\n",
       "294   147_left.jpg  1  0  0  0  0  0  0  0\n",
       "295  147_right.jpg  0  0  0  0  0  0  0  1\n",
       "296   148_left.jpg  0  0  0  0  0  0  0  1\n",
       "297  148_right.jpg  0  1  0  0  0  0  0  0\n",
       "298   149_left.jpg  1  0  0  0  0  0  0  0\n",
       "299  149_right.jpg  0  0  0  0  0  0  0  1\n",
       "300   150_left.jpg  0  0  0  0  0  0  0  1\n",
       "301  150_right.jpg  1  0  0  0  0  0  0  0\n",
       "302   151_left.jpg  1  0  0  0  0  0  0  0\n",
       "303  151_right.jpg  0  1  0  0  0  0  0  1\n",
       "304   152_left.jpg  0  0  0  0  1  0  0  0\n",
       "305  152_right.jpg  0  0  0  0  1  0  0  0\n",
       "306   153_left.jpg  0  0  1  0  0  0  0  0\n",
       "307  153_right.jpg  0  0  0  0  1  0  0  0\n",
       "308   154_left.jpg  0  0  0  0  0  0  0  1\n",
       "309  154_right.jpg  1  0  0  0  0  0  0  0\n",
       "\n",
       "[310 rows x 9 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eye_by_eye.head(31)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3097\n",
      "1640\n",
      "326\n",
      "313\n",
      "280\n",
      "193\n",
      "253\n",
      "1360\n"
     ]
    }
   ],
   "source": [
    "labels = ['N', 'D', 'G', 'C', 'A', 'H', 'M', 'O']\n",
    "for label in labels:\n",
    "    print(eye_by_eye[label].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    6466\n",
       "2     486\n",
       "3       8\n",
       "0       2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summ = eye_by_eye.apply(lambda x: x['N'] + x['D'] + x['G'] + x['C'] + x['A'] + x['H'] + x['M'] + x['O'], axis=1)\n",
    "summ.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eye_by_eye.to_csv('splited_train.csv', indde)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub = pd.read_csv('data/XYZ_ODIR.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy = [1] * sub.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub['N'] = dummy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sub.to_csv('baseline.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
